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Hurricane Harvey provides lab for U.S. forecast experiments

For years, U.S. forecasters have envied their colleagues at the European Centre for Medium-Range Weather Forecasts (ECMWF) in Reading, U.K., whose hurricane prediction models remain the gold standard. Infamously, the National Weather Service (NWS) in 2012 failed to predict Hurricane Sandy’s turn into New Jersey, whereas ECMWF was spot on. But two innovations tested during Hurricane Harvey, one from NASA and another from the National Oceanic and Atmospheric Administration (NOAA), could help level the playing field.

NOAA’s offering is a brand-new forecasting model. Two years ago, NOAA’s Geophysical Fluid Dynamics Laboratory (GFDL) in Princeton, New Jersey, won a competition to provide the computer code for the next-generation weather model of NWS. Current NWS models must wait for results from a time-consuming global simulation before they can zoom in on a smaller area and run a high-resolution model for hurricanes. With GFDL’s new code, the next-generation model will be able to simulate storms at the same time as it runs globally, in theory, improving forecasts for hurricane paths because its fine-scaled predictions feed immediately into the model’s next run, rather than lagging behind.

Last week, GFDL anxiously watched the developing storm to see how it compared with a test run of the next-generation model. On Thursday, a day prior to landfall, the experiment agreed with the European model that Harvey would plow inland, stall, then head back out over the Gulf of Mexico before making a second landfall near Houston, Texas. That progression, close to what’s happening, helps explain the sustained, catastrophic rainfall that has battered the Texas coast.

The GFDL model, called FV3, also correctly forecasted that Harvey would develop a double eyewall—a second circular band of storms around the band enclosing the eye. The model’s zoomed-in view also predicted the extreme rainfall totals seen by Houston some 5 days in advance, says Shian-Jiann Lin, the GFDL scientist who led the development of the code powering FV3.

View of the eyewall of 2005’s Hurricane Katrina from a P-3 hurricane hunter.

NOAA

Caution should be taken in interpreting such results, though, says Chris Davis, a meteorologist at the National Center for Atmospheric Research in Boulder, Colorado. “I do not believe meaningful conclusions about model performance can be reached for a single storm.” Still, Lin says, “If you count the full history of Harvey … I think FV3 global is likely the top performer.” FV3 may help with hurricane prediction when it starts powering U.S. forecasts, probably next year.

Intensity can be even harder to predict than storm paths, and here NASA may be able to help. Many models missed that Harvey would grow to a category-4 storm just prior to landfall, in part because data on wind speeds are spotty and difficult to collect. Last December, NASA launched a constellation of eight identical microsatellites, called the Cyclone Global Navigation Satellite System (CYGNSS), to fill the gap. CYGNSS works by detecting the surface roughness of the ocean—a proxy for wind speeds—from the reflected radio signals of GPS satellites. These long-wavelength signals can pass through the veil of rain that cloaks hurricanes and blocks the microwaves that traditional weather satellites detect.

Harvey was the first test for CYGNSS in severe winds. On 25 August, before Harvey made landfall, Christopher Ruf, an atmospheric scientist and engineer at the University of Michigan in Ann Arbor, strapped himself into a P-3 turboprop, an NOAA hurricane hunter, bound for the storm’s eye. His seat fell out beneath him again and again as the aircraft repeatedly plunged into the eyewall. Each time the wind grew more severe: The storm was rapidly intensifying.

It will take weeks to know whether CYGNSS captured this sharp intensification, Ruf says. The weather service will be following his results closely. The constellation is technically only a 2-year experiment, but it’s possible the satellites could be pressed into operational service for NOAA, Ruf says. “Our simulations have shown that the forecast skill is improved. Now we need to demonstrate it for real.”